from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-25 14:06:36.570123
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 25, Nov, 2020
Time: 14:06:39
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.6647
Nobs: 121.000 HQIC: -43.8996
Log likelihood: 1251.80 FPE: 3.70955e-20
AIC: -44.7442 Det(Omega_mle): 1.81530e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.683358 0.205228 3.330 0.001
L1.Burgenland 0.133749 0.091040 1.469 0.142
L1.Kärnten -0.305388 0.076466 -3.994 0.000
L1.Niederösterreich 0.021974 0.218727 0.100 0.920
L1.Oberösterreich 0.266441 0.179815 1.482 0.138
L1.Salzburg 0.127048 0.089637 1.417 0.156
L1.Steiermark 0.087220 0.128188 0.680 0.496
L1.Tirol 0.164325 0.084677 1.941 0.052
L1.Vorarlberg 0.016842 0.083545 0.202 0.840
L1.Wien -0.162716 0.173200 -0.939 0.347
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.701214 0.261806 2.678 0.007
L1.Burgenland -0.010390 0.116137 -0.089 0.929
L1.Kärnten 0.347883 0.097546 3.566 0.000
L1.Niederösterreich 0.093567 0.279027 0.335 0.737
L1.Oberösterreich -0.216881 0.229386 -0.945 0.344
L1.Salzburg 0.164373 0.114348 1.437 0.151
L1.Steiermark 0.189977 0.163527 1.162 0.245
L1.Tirol 0.134619 0.108020 1.246 0.213
L1.Vorarlberg 0.198375 0.106577 1.861 0.063
L1.Wien -0.573256 0.220948 -2.595 0.009
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.330417 0.087836 3.762 0.000
L1.Burgenland 0.108114 0.038964 2.775 0.006
L1.Kärnten -0.024884 0.032727 -0.760 0.447
L1.Niederösterreich 0.141839 0.093614 1.515 0.130
L1.Oberösterreich 0.265356 0.076959 3.448 0.001
L1.Salzburg -0.007478 0.038364 -0.195 0.845
L1.Steiermark -0.060143 0.054863 -1.096 0.273
L1.Tirol 0.095474 0.036241 2.634 0.008
L1.Vorarlberg 0.149964 0.035757 4.194 0.000
L1.Wien 0.009901 0.074128 0.134 0.894
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.210989 0.104151 2.026 0.043
L1.Burgenland 0.006909 0.046202 0.150 0.881
L1.Kärnten 0.035700 0.038806 0.920 0.358
L1.Niederösterreich 0.089937 0.111002 0.810 0.418
L1.Oberösterreich 0.348533 0.091254 3.819 0.000
L1.Salzburg 0.084518 0.045490 1.858 0.063
L1.Steiermark 0.196134 0.065054 3.015 0.003
L1.Tirol 0.027196 0.042972 0.633 0.527
L1.Vorarlberg 0.113194 0.042398 2.670 0.008
L1.Wien -0.113143 0.087897 -1.287 0.198
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.843574 0.225956 3.733 0.000
L1.Burgenland 0.057654 0.100235 0.575 0.565
L1.Kärnten -0.014290 0.084189 -0.170 0.865
L1.Niederösterreich -0.108190 0.240819 -0.449 0.653
L1.Oberösterreich 0.046210 0.197976 0.233 0.815
L1.Salzburg 0.041044 0.098691 0.416 0.677
L1.Steiermark 0.113593 0.141135 0.805 0.421
L1.Tirol 0.232800 0.093229 2.497 0.013
L1.Vorarlberg 0.037112 0.091984 0.403 0.687
L1.Wien -0.218128 0.190693 -1.144 0.253
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.183535 0.154624 1.187 0.235
L1.Burgenland -0.039123 0.068591 -0.570 0.568
L1.Kärnten -0.008566 0.057611 -0.149 0.882
L1.Niederösterreich 0.206871 0.164795 1.255 0.209
L1.Oberösterreich 0.392349 0.135477 2.896 0.004
L1.Salzburg -0.039726 0.067535 -0.588 0.556
L1.Steiermark -0.053642 0.096580 -0.555 0.579
L1.Tirol 0.195543 0.063797 3.065 0.002
L1.Vorarlberg 0.056435 0.062945 0.897 0.370
L1.Wien 0.116633 0.130493 0.894 0.371
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.323244 0.196705 1.643 0.100
L1.Burgenland 0.066848 0.087259 0.766 0.444
L1.Kärnten -0.079736 0.073290 -1.088 0.277
L1.Niederösterreich -0.129562 0.209643 -0.618 0.537
L1.Oberösterreich -0.124117 0.172347 -0.720 0.471
L1.Salzburg -0.002604 0.085914 -0.030 0.976
L1.Steiermark 0.381107 0.122864 3.102 0.002
L1.Tirol 0.536687 0.081160 6.613 0.000
L1.Vorarlberg 0.227540 0.080076 2.842 0.004
L1.Wien -0.188637 0.166006 -1.136 0.256
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.160065 0.226355 0.707 0.479
L1.Burgenland 0.017347 0.100411 0.173 0.863
L1.Kärnten -0.064928 0.084338 -0.770 0.441
L1.Niederösterreich 0.251220 0.241244 1.041 0.298
L1.Oberösterreich 0.006031 0.198326 0.030 0.976
L1.Salzburg 0.231119 0.098865 2.338 0.019
L1.Steiermark 0.156905 0.141384 1.110 0.267
L1.Tirol 0.050854 0.093393 0.545 0.586
L1.Vorarlberg 0.009027 0.092146 0.098 0.922
L1.Wien 0.196184 0.191030 1.027 0.304
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.658524 0.125308 5.255 0.000
L1.Burgenland -0.008806 0.055587 -0.158 0.874
L1.Kärnten -0.006861 0.046688 -0.147 0.883
L1.Niederösterreich -0.055705 0.133550 -0.417 0.677
L1.Oberösterreich 0.260845 0.109791 2.376 0.018
L1.Salzburg 0.005383 0.054731 0.098 0.922
L1.Steiermark 0.007424 0.078269 0.095 0.924
L1.Tirol 0.078343 0.051702 1.515 0.130
L1.Vorarlberg 0.188598 0.051011 3.697 0.000
L1.Wien -0.116416 0.105752 -1.101 0.271
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.079932 -0.054838 0.195902 0.236074 0.018012 0.066710 -0.135532 0.100274
Kärnten 0.079932 1.000000 -0.066301 0.172732 0.069440 -0.161726 0.171794 0.011244 0.273595
Niederösterreich -0.054838 -0.066301 1.000000 0.228069 0.060037 0.148595 0.070755 0.049286 0.356134
Oberösterreich 0.195902 0.172732 0.228069 1.000000 0.239623 0.264980 0.066699 0.054342 0.030302
Salzburg 0.236074 0.069440 0.060037 0.239623 1.000000 0.137655 0.041109 0.069941 -0.072374
Steiermark 0.018012 -0.161726 0.148595 0.264980 0.137655 1.000000 0.096004 0.095664 -0.200878
Tirol 0.066710 0.171794 0.070755 0.066699 0.041109 0.096004 1.000000 0.131758 0.081753
Vorarlberg -0.135532 0.011244 0.049286 0.054342 0.069941 0.095664 0.131758 1.000000 0.070411
Wien 0.100274 0.273595 0.356134 0.030302 -0.072374 -0.200878 0.081753 0.070411 1.000000